Statistical applications in knowledge translation research implemented through the information assessment method
Bibliographic record
Abstract
Of interest are two knowledge translation [27] research projects conducted by and with the ITPCRG (Information Technology Primary Care Research Group) during the period 2010-2012, as well as their underlying statistical analyses. For physicians, continuing medical education (CME) is a critical activity that helps them acquire new knowledge and keep their practice up to date. In Canada, popular CME programs are structured around the reading of short synopses or summaries of important clinical research on e-mail. After reading one synopsis, the physician completes a short reective exercise, using the Information Assessment Method (IAM). IAMis a brief questionnaire that asks physicians to reect on the following: -Therelevance of the information? -The impact of the information e.g. did you learn something new? -If they intend to use the information for a specic patient? -Whether they expect to see health benets for that patient as aresult? This type of CME is very popular. Since September 2006, about4,500 members of the Canadian Medical Association have submitted more than one million IAM questionnaires linked to e-mailed synopses. Previous work suggests the response format of the IAM questionnaire can impact the willingness of physicians to participate, and that information use for a specic patient might be linked to certain factors measurable by IAM. Therefore, the objectives were to improve CME programs that use the IAM questionnaire by determining which response formats optimize physician participation and their reective learning, and explore the determinants of information use. These were accomplished by implementing a survival analysis framework, as well as mixed logistic regression models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".